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" }, "metadata": {}, "execution_count": 1 } ], "source": [ "import pandas as pd\n", "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n", "cuisines_df.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 indian\n", "1 indian\n", "2 indian\n", "3 indian\n", "4 indian\n", "Name: cuisine, dtype: object" ] }, "metadata": {}, "execution_count": 2 } ], "source": [ "cuisines_label_df = cuisines_df['cuisine']\n", "cuisines_label_df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " almond angelica anise anise_seed apple apple_brandy apricot \\\n", "0 0 0 0 0 0 0 0 \n", "1 1 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 0 0 \n", "\n", " armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n", "0 0 0 0 ... 0 0 0 \n", "1 0 0 0 ... 0 0 0 \n", "2 0 0 0 ... 0 0 0 \n", "3 0 0 0 ... 0 0 0 \n", "4 0 0 0 ... 0 0 0 \n", "\n", " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n", "0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 1 0 \n", "\n", "[5 rows x 380 columns]" ], "text/html": "
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" }, "metadata": {}, "execution_count": 3 } ], "source": [ "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n", "cuisines_feature_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Coba pengklasifikasi yang berbeda\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.model_selection import train_test_split, cross_val_score\n", "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "\n", "C = 10\n", "# Create different classifiers.\n", "classifiers = {\n", " 'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n", " 'KNN classifier': KNeighborsClassifier(C),\n", " 'SVC': SVC(),\n", " 'RFST': RandomForestClassifier(n_estimators=100),\n", " 'ADA': AdaBoostClassifier(n_estimators=100)\n", " \n", "}\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy (train) for Linear SVC: 76.4% \n", " precision recall f1-score support\n", "\n", " chinese 0.64 0.66 0.65 242\n", " indian 0.91 0.86 0.89 236\n", " japanese 0.72 0.73 0.73 245\n", " korean 0.83 0.75 0.79 234\n", " thai 0.75 0.82 0.78 242\n", "\n", " accuracy 0.76 1199\n", " macro avg 0.77 0.76 0.77 1199\n", "weighted avg 0.77 0.76 0.77 1199\n", "\n", "Accuracy (train) for KNN classifier: 70.7% \n", " precision recall f1-score support\n", "\n", " chinese 0.65 0.63 0.64 242\n", " indian 0.84 0.81 0.82 236\n", " japanese 0.60 0.81 0.69 245\n", " korean 0.89 0.53 0.67 234\n", " thai 0.69 0.75 0.72 242\n", "\n", " accuracy 0.71 1199\n", " macro avg 0.73 0.71 0.71 1199\n", "weighted avg 0.73 0.71 0.71 1199\n", "\n", "Accuracy (train) for SVC: 80.1% \n", " precision recall f1-score support\n", "\n", " chinese 0.71 0.69 0.70 242\n", " indian 0.92 0.92 0.92 236\n", " japanese 0.77 0.78 0.77 245\n", " korean 0.87 0.77 0.82 234\n", " thai 0.75 0.86 0.80 242\n", "\n", " accuracy 0.80 1199\n", " macro avg 0.80 0.80 0.80 1199\n", "weighted avg 0.80 0.80 0.80 1199\n", "\n", "Accuracy (train) for RFST: 82.8% \n", " precision recall f1-score support\n", "\n", " chinese 0.80 0.75 0.77 242\n", " indian 0.90 0.91 0.90 236\n", " japanese 0.82 0.78 0.80 245\n", " korean 0.85 0.82 0.83 234\n", " thai 0.78 0.89 0.83 242\n", "\n", " accuracy 0.83 1199\n", " macro avg 0.83 0.83 0.83 1199\n", "weighted avg 0.83 0.83 0.83 1199\n", "\n", "Accuracy (train) for ADA: 71.1% \n", " precision recall f1-score support\n", "\n", " chinese 0.60 0.57 0.58 242\n", " indian 0.87 0.84 0.86 236\n", " japanese 0.71 0.60 0.65 245\n", " korean 0.68 0.78 0.72 234\n", " thai 0.70 0.78 0.74 242\n", "\n", " accuracy 0.71 1199\n", " macro avg 0.71 0.71 0.71 1199\n", "weighted avg 0.71 0.71 0.71 1199\n", "\n" ] } ], "source": [ "n_classifiers = len(classifiers)\n", "\n", "for index, (name, classifier) in enumerate(classifiers.items()):\n", " classifier.fit(X_train, np.ravel(y_train))\n", "\n", " y_pred = classifier.predict(X_test)\n", " accuracy = accuracy_score(y_test, y_pred)\n", " print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n", " print(classification_report(y_test,y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**Penafian**: \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berusaha untuk memberikan hasil yang akurat, harap diingat bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang otoritatif. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan profesional oleh manusia. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.\n" ] } ], "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" }, "kernelspec": { "name": "python3", "display_name": "Python 3.7.0 64-bit ('3.7')" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" } }, "coopTranslator": { "original_hash": "7ea2b714669c823a596d986ba2d5739f", "translation_date": "2025-09-04T08:33:04+00:00", "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb", "language_code": "id" } }, "nbformat": 4, "nbformat_minor": 4 }